Robert Peharz
Robert Peharz
Assistant Professor of AI, TU Graz
Verified email at - Homepage
Cited by
Cited by
Sparse nonnegative matrix factorization with ℓ0-constraints
R Peharz, F Pernkopf
Neurocomputing 80, 38-46, 2012
Sparse nonnegative matrix factorization using ℓ0-constraints
R Peharz, M Stark, F Pernkopf
Machine Learning for Signal Processing (MLSP), 2010 IEEE International …, 2010
Fidgety movements–tiny in appearance, but huge in impact
C Einspieler, R Peharz, PB Marschik
Jornal de Pediatria 92, 64-70, 2016
On the latent variable interpretation in sum-product networks
R Peharz, R Gens, F Pernkopf, P Domingos
IEEE transactions on pattern analysis and machine intelligence 39 (10), 2030 …, 2016
On Theoretical Properties of Sum-Product Networks
R Peharz, S Tschiatschek, F Pernkopf, P Domingos
Proceedings of the Eighteenth International Conference on Artificial …, 2015
Random sum-product networks: A simple and effective approach to probabilistic deep learning
R Peharz, A Vergari, K Stelzner, A Molina, X Shao, M Trapp, K Kersting, ...
Uncertainty in Artificial Intelligence, 334-344, 2020
A novel way to measure and predict development: A heuristic approach to facilitate the early detection of neurodevelopmental disorders
PB Marschik, FB Pokorny, R Peharz, D Zhang, J O’Muircheartaigh, ...
Current neurology and neuroscience reports 17, 1-15, 2017
Einsum networks: Fast and scalable learning of tractable probabilistic circuits
R Peharz, S Lang, A Vergari, K Stelzner, A Molina, M Trapp, ...
International Conference on Machine Learning, 7563-7574, 2020
Greedy part-wise learning of sum-product networks
R Peharz, BC Geiger, F Pernkopf
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2013
Modeling speech with sum-product networks: Application to bandwidth extension
R Peharz, G Kapeller, P Mowlaee, F Pernkopf
2014 IEEE International Conference on Acoustics, Speech and Signal …, 2014
Learning selective sum-product networks
R Peharz, R Gens, P Domingos
LTPM workshop 32, 2014
Foundations of Sum-Product Networks for Probabilistic Modeling
R Peharz
Graz University of Technology, SPSC, 2015
Spflow: An easy and extensible library for deep probabilistic learning using sum-product networks
A Molina, A Vergari, K Stelzner, R Peharz, P Subramani, N Di Mauro, ...
arXiv preprint arXiv:1901.03704, 2019
Minimal random code learning: Getting bits back from compressed model parameters
M Havasi, R Peharz, JM Hernández-Lobato
arXiv preprint arXiv:1810.00440, 2018
Novel AI driven approach to classify infant motor functions
S Reich, D Zhang, T Kulvicius, S Bölte, K Nielsen-Saines, FB Pokorny, ...
Scientific Reports 11 (1), 9888, 2021
Faster attend-infer-repeat with tractable probabilistic models
K Stelzner, R Peharz, K Kersting
International Conference on Machine Learning, 5966-5975, 2019
Bayesian learning of sum-product networks
M Trapp, R Peharz, H Ge, F Pernkopf, Z Ghahramani
Advances in neural information processing systems 32, 2019
Resource-efficient neural networks for embedded systems
W Roth, G Schindler, M Zöhrer, L Pfeifenberger, R Peharz, S Tschiatschek, ...
arXiv preprint arXiv:2001.03048, 2020
Introduction to probabilistic graphical models
F Pernkopf, R Peharz, S Tschiatschek
Academic Press Library in Signal Processing 1, 989-1064, 2014
Conditional sum-product networks: Imposing structure on deep probabilistic architectures
X Shao, A Molina, A Vergari, K Stelzner, R Peharz, T Liebig, K Kersting
International Conference on Probabilistic Graphical Models, 401-412, 2020
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